CRATE: Accurate and efficient clustering-based nonlinear analysis of heterogeneous materials through computational homogenization

The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.

Code used to identify and analyze drought clusters from gridded data.

Optimize clustering labels using Silhouette Score.

Cluster Validity Index Using a Distance-based Separability Measure

Clustering and resource allocation using Deterministic Annealing Approach and Orthogonal Non-negative Matrix Factorization O-(NMF)

Defines a boundary around cluster centers in a given point-layer shapefile.

Random Neighbors: Random Forest style clustering for high-dimensional data

code for PhD thesis

Cheminformatics based project that aims to assess the diversity of the known inhibitors of SarsCov-2 proteases taken from COVID Moonshot project.

Mutual Information-based Non-linear Clustering Analysis

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

Clustering - Cohort Analysis - Retention Analysis

An application for clustering keywords in polish based on text morphology or semantic connections.

Library of popular algorithms implemented in a parallel way

Implemented an auto-clustering tool with seed and number of clusters finder. Optimizing algorithms: Silhouette, Elbow. Clustering algorithms: k-Means, Bisecting k-Means, Gaussian Mixture. Module includes micro-macro pivoting, and dashboards displaying radius, centroids, and inertia of clusters. Used: Python, Pyspark, Matplotlib, Spark MLlib.

A concatenation of two GNNs to decode dynamic clustering on localization datasets

K-Means Clustering & Dimensionality Reduction and Market Basket Analysis - Project Submission for Data Mining & Machine Learning Module

Partitioning a set of objects into groups(clusters) of diverse objects. The aim is to maximize intra-cluster diversity while at the same time maintaining the inter-cluster similarity.

Práctica de clustering de la asignatura Inteligencia de Negocio de cuarto curso de Ingeniería Informática.